OpenRaiser/Intern-Atlas huggingface dataset:
Session-based Complementary Fashion Recommendations
Venue: arXiv cs.IR 2019
Year: 2019

In modern fashion e-commerce platforms, where customers can browse thousands to millions of products, recommender systems are useful tools to navigate and narrow down the vast assortment. In this scenario, complementary recommendations serve the user need to find items that can be worn together. In this paper, we present a personalized, session-based complementary item recommendation algorithm, ZSF-c, tailored for the fashion usecase. We propose a sampling strategy adopted to build the training set, which is useful when existing user interaction data cannot be directly used due to poor quality or availability. Our proposed approach shows significant improvements in terms of accuracy compared to the collaborative filtering approach, serving complementary item recommendations to our customers at the time of the experiments CF-c. The results show an offline relative uplift of +8.2% in Orders Recall@5, as well as a significant +3.24% increase in the number of purchased products measured in an online A/B test carried out in a fashion e-commerce platform with 28 million active customers.
Do Energy-oriented Changes Hinder Maintainability?
Venue: arXiv cs.SE 2019
Year: 2019

Energy efficiency is a crucial quality requirement for mobile applications. However, improving energy efficiency is far from trivial as developers lack the knowledge and tools to aid in this activity. In this paper we study the impact of changes to improve energy efficiency on the maintainability of Android applications. Using a dataset containing 539 energy efficiency-oriented commits, we measure maintainability -- as computed by the Software Improvement Group's web-based source code analysis service Better Code Hub (BCH) -- before and after energy efficiency-related code changes. Results show that in general improving energy efficiency comes with a significant decrease in maintainability. This is particularly evident in code changes to accommodate the Power Save Mode and Wakelock Addition energy patterns. In addition, we perform manual analysis to assess how real examples of energy-oriented changes affect maintainability. Our results help mobile app developers to 1) avoid common maintainability issues when improving the energy efficiency of their apps; and 2) adopt development processes to build maintainable and energy-efficient code. We also support researchers by identifying challenges in mobile app development that still need to be addressed.
Efficient Cross-Validation of Echo State Networks
Venue: arXiv cs.LG 2019
Year: 2019

Echo State Networks (ESNs) are known for their fast and precise one-shot learning of time series. But they often need good hyper-parameter tuning for best performance. For this good validation is key, but usually, a single validation split is used. In this rather practical contribution we suggest several schemes for cross-validating ESNs and introduce an efficient algorithm for implementing them. The component that dominates the time complexity of the already quite fast ESN training remains constant (does not scale up with $k$) in our proposed method of doing $k$-fold cross-validation. The component that does scale linearly with $k$ starts dominating only in some not very common situations. Thus in many situations $k$-fold cross-validation of ESNs can be done for virtually the same time complexity as a simple single split validation. Space complexity can also remain the same. We also discuss when the proposed validation schemes for ESNs could be beneficial and empirically investigate them on several different real-world datasets.
Data Context Adaptation for Accurate Recommendation with Additional Information
Venue: arXiv cs.LG 2019
Year: 2019

Given a sparse rating matrix and an auxiliary matrix of users or items, how can we accurately predict missing ratings considering different data contexts of entities? Many previous studies proved that utilizing the additional information with rating data is helpful to improve the performance. However, existing methods are limited in that 1) they ignore the fact that data contexts of rating and auxiliary matrices are different, 2) they have restricted capability of expressing independence information of users or items, and 3) they assume the relation between a user and an item is linear. We propose DaConA, a neural network based method for recommendation with a rating matrix and an auxiliary matrix. DaConA is designed with the following three main ideas. First, we propose a data context adaptation layer to extract pertinent features for different data contexts. Second, DaConA represents each entity with latent interaction vector and latent independence vector. Unlike previous methods, both of the two vectors are not limited in size. Lastly, while previous matrix factorization based methods predict missing values through the inner-product of latent vectors, DaConA learns a non-linear function of them via a neural network. We show that DaConA is a generalized algorithm including the standard matrix factorization and the collective matrix factorization as special cases. Through comprehensive experiments on real-world datasets, we show that DaConA provides the state-of-the-art accuracy.
A Decomposition and Metric-Based Evaluation Framework for Microservices
Venue: arXiv cs.SE 2019
Year: 2019

Migrating from monolithic systems into microservice is a very complex task. Companies are commonly decomposing the monolithic system manually, analyzing dependencies of the monolith and then assessing different decomposition options. The goal of our work is two-folded: 1) we provide a microservice measurement framework to objectively evaluate and compare the quality of microservices-based systems; 2) we propose a decomposition system based on business process mining. The microservice measurement framework can be applied independently from the decomposition process adopted, but is also useful to continuously evaluate the architectural evolution of a system. Results show that the decomposition framework helps companies to easily identify the different decomposition options. The measurement framework can help to decrease the subjectivity of the decision between different decomposition options and to evaluate architectural erosion in existing systems.
Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement
Venue: arXiv cs.CV 2019
Year: 2019

Generic Image recognition is a fundamental and fairly important visual problem in computer vision. One of the major challenges of this task lies in the fact that single image usually has multiple objects inside while the labels are still one-hot, another one is noisy and sometimes missing labels when annotated by humans. In this paper, we focus on tackling these challenges accompanying with two different image recognition problems: multi-model ensemble and noisy data recognition with a unified framework. As is well-known, usually the best performing deep neural models are ensembles of multiple base-level networks, as it can mitigate the variation or noise containing in the dataset. Unfortunately, the space required to store these many networks, and the time required to execute them at runtime, prohibit their use in applications where test sets are large (e.g., ImageNet). In this paper, we present a method for compressing large, complex trained ensembles into a single network, where the knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously. Extensive experiments on CIFAR-10/100, SVHN, ImageNet and iMaterialist Challenge Dataset demonstrate the effectiveness of our MEAL method. On ImageNet, our ResNet-50 based MEAL achieves top-1/5 21.79%/5.99% val error, which outperforms the original model by 2.06%/1.14%. On iMaterialist Challenge Dataset, our MEAL obtains a remarkable improvement of top-3 1.15% (official evaluation metric) on a strong baseline model of ResNet-101.
Design, Assembly, Calibration, and Measurement of an Augmented Reality Haploscope
Venue: arXiv cs.GR 2019
Year: 2019

A haploscope is an optical system which produces a carefully controlled virtual image. Since the development of Wheatstone's original stereoscope in 1838, haploscopes have been used to measure perceptual properties of human stereoscopic vision. This paper presents an augmented reality (AR) haploscope, which allows the viewing of virtual objects superimposed against the real world. Our lab has used generations of this device to make a careful series of perceptual measurements of AR phenomena, which have been described in publications over the previous 8 years. This paper systematically describes the design, assembly, calibration, and measurement of our AR haploscope. These methods have been developed and improved in our lab over the past 10 years. Despite the fact that 180 years have elapsed since the original report of Wheatstone's stereoscope, we have not previously found a paper that describes these kinds of details.
Feedbackward Decoding for Semantic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

We propose a novel approach for semantic segmentation that uses an encoder in the reverse direction to decode. Many semantic segmentation networks adopt a feedforward encoder-decoder architecture. Typically, an input is first downsampled by the encoder to extract high-level semantic features and continues to be fed forward through the decoder module to recover low-level spatial clues. Our method works in an alternative direction that lets information flow backward from the last layer of the encoder towards the first. The encoder performs encoding in the forward pass and the same network performs decoding in the backward pass. Therefore, the encoder itself is also the decoder. Compared to conventional encoder-decoder architectures, ours doesn't require additional layers for decoding and further reuses the encoder weights thereby reducing the total number of parameters required for processing. We show by using only the 13 convolutional layers from VGG-16 plus one tiny classification layer, our model significantly outperforms other frequently cited models that are also adapted from VGG-16. On the Cityscapes semantic segmentation benchmark, our model uses 50.0% less parameters than SegNet and achieves an 18.1% higher "IoU class" score; it uses 28.3% less parameters than DeepLab LargeFOV and the achieved "IoU class" score is 3.9% higher; it uses 89.1% fewer parameters than FCN-8s and the achieved "IoU class" score is 3.1% higher. Our code will be publicly available on Github later.
Song Hit Prediction: Predicting Billboard Hits Using Spotify Data
Venue: arXiv cs.IR 2019
Year: 2019

In this work, we attempt to solve the Hit Song Science problem, which aims to predict which songs will become chart-topping hits. We constructed a dataset with approximately 1.8 million hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four models on our dataset. Our best model was random forest, which was able to predict Billboard song success with 88% accuracy.
On the importance of system-view centric validation for the design and operation of a crypto-based digital economy
Venue: arXiv cs.CY 2019
Year: 2019

Ubiquitous connectivity, networked computation, open technologies and advances in intelligent web approaches enable the third-generation web, Web3.0. Not least due to advancements in crypto technologies adoption in real world applications, it is now possible to convert the internet of things (IoT) into an economy of things (EoT), which basically refers to a heterogeneous digital economy of everything (dEoE). Based on the realization of the deficiencies of Web2.0 and the loss of confidence in a central intermediaries based economy, a societal desire to overcome these deficiencies has emerged. In the beginning, the crypto movement has been enthusiastic about the ability to encode all necessities for a digital economy in algorithms and code. However, in the mean time, insight has gained ground that on the one hand, designing the EoT is a quite complex endeavor and the resulting system might fail to achieve the targeted ideals, even when no intermediaries are present. We emphasize this position by arguing that the EoT in fact corresponds to a complex system operated in open contexts, for which a sophisticated design approach and a set of measures are inevitable, in order to be able to achieve a valid design and operation. In addition, we argue that the high level guiding principles necessary for a valid and effective EoT align well with the ideals of the democratized Web3.0 movement, and are no naive rapture, but rather - adequate adoption provided - can lead to socioeconomic efficiency. Over and above, we postulate that in this kind of EoT, commercial companies not only play a role, but can benefit alongside the society. In more game theoretical jargon, one could state that it is rational for the society and enterprises to undertake the effort to establish a valid EoT based on these ideals.
Gender Representation in French Broadcast Corpora and Its Impact on ASR Performance
Venue: arXiv cs.CL 2019
Year: 2019

This paper analyzes the gender representation in four major corpora of French broadcast. These corpora being widely used within the speech processing community, they are a primary material for training automatic speech recognition (ASR) systems. As gender bias has been highlighted in numerous natural language processing (NLP) applications, we study the impact of the gender imbalance in TV and radio broadcast on the performance of an ASR system. This analysis shows that women are under-represented in our data in terms of speakers and speech turns. We introduce the notion of speaker role to refine our analysis and find that women are even fewer within the Anchor category corresponding to prominent speakers. The disparity of available data for both gender causes performance to decrease on women. However this global trend can be counterbalanced for speaker who are used to speak in the media when sufficient amount of data is available.
Opponent Aware Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

We introduce Threatened Markov Decision Processes (TMDPs) as an extension of the classical Markov Decision Process framework for Reinforcement Learning (RL). TMDPs allow suporting a decision maker against potential opponents in a RL context. We also propose a level-k thinking scheme resulting in a novel learning approach to deal with TMDPs. After introducing our framework and deriving theoretical results, relevant empirical evidence is given via extensive experiments, showing the benefits of accounting for adversaries in RL while the agent learns
Beyond the Channel Capacity of BPSK Input
Venue: arXiv cs.IT 2019
Year: 2019

The paper proposed a method that organizes a parallel transmission of two signals to be separated from each other at receiver through Hamming- to Euclidean space, where the conventional problem of achievable bit rate (ABR) is converted to that of the ABR's summation with respect to the two separated signals. Actually, the mutual information is separated accordingly into two. Since the mutual information is non-linear function of signal power in general, the separation brings a chance for achieving higher ABR. The proposed work proceeds along the above thinking and achieves higher bit-error-rate (BER) performance in comparison with BPSK as shown in the simulations. Moreover, one can find theoretically beyond of channel capacity of BPSK input.
Generating High-Resolution Fashion Model Images Wearing Custom Outfits
Venue: arXiv cs.CV 2019
Year: 2019

Visualizing an outfit is an essential part of shopping for clothes. Due to the combinatorial aspect of combining fashion articles, the available images are limited to a pre-determined set of outfits. In this paper, we broaden these visualizations by generating high-resolution images of fashion models wearing a custom outfit under an input body pose. We show that our approach can not only transfer the style and the pose of one generated outfit to another, but also create realistic images of human bodies and garments.
On the edge-vertex ratio of maximal thrackles
Venue: arXiv cs.DM 2019
Year: 2019

A drawing of a graph in the plane is a thrackle if every pair of edges intersects exactly once, either at a common vertex or at a proper crossing. Conway's conjecture states that a thrackle has at most as many edges as vertices. In this paper, we investigate the edge-vertex ratio of maximal thrackles, that is, thrackles in which no edge between already existing vertices can be inserted such that the resulting drawing remains a thrackle. For maximal geometric and topological thrackles, we show that the edge-vertex ratio can be arbitrarily small. When forbidding isolated vertices, the edge-vertex ratio of maximal geometric thrackles can be arbitrarily close to the natural lower bound of 1/2. For maximal topological thrackles without isolated vertices, we present an infinite family with an edge-vertex ratio of 5/6.
Complexity and Geometry of Sampling Connected Graph Partitions
Venue: arXiv cs.CC 2019
Year: 2019

In this paper, we prove intractability results about sampling from the set of partitions of a planar graph into connected components. Our proofs are motivated by a technique introduced by Jerrum, Valiant, and Vazirani. Moreover, we use gadgets inspired by their technique to provide families of graphs where the "flip walk" Markov chain used in practice for this sampling task exhibits exponentially slow mixing. Supporting our theoretical results we present some empirical evidence demonstrating the slow mixing of the flip walk on grid graphs and on real data. Inspired by connections to the statistical physics of self-avoiding walks, we investigate the sensitivity of certain popular sampling algorithms to the graph topology. Finally, we discuss a few cases where the sampling problem is tractable. Applications to political redistricting have recently brought increased attention to this problem, and we articulate open questions about this application that are highlighted by our results.
$α$ Belief Propagation as Fully Factorized Approximation
Venue: arXiv cs.LG 2019
Year: 2019

Belief propagation (BP) can do exact inference in loop-free graphs, but its performance could be poor in graphs with loops, and the understanding of its solution is limited. This work gives an interpretable belief propagation rule that is actually minimization of a localized $α$-divergence. We term this algorithm as $α$ belief propagation ($α$-BP). The performance of $α$-BP is tested in MAP (maximum a posterior) inference problems, where $α$-BP can outperform (loopy) BP by a significant margin even in fully-connected graphs.
Identifying Indoor Points of Interest via Mobile Crowdsensing: An Experimental Study
Venue: arXiv cs.CY 2019
Year: 2019

This paper presents a mobile crowdsensing approach to identify the indoor points of interest (POI) by exploiting Wi-Fi similarity measurements. Since indoor environments are lacking the GPS positioning accuracy when compared to outdoors, we rely on widely available Wi-Fi access points (AP) in contemporary urban indoor environments, to accurately identify user POI. We propose a smartphone application based system architecture to scan the surrounding Wi-Fi AP and measure the cosine similarity of received signal strengths (RSS), and demonstrate through the experimental results that it is possible to identify the distinct POI of users, and the common POI among users of a given indoor environment.
Sparse Generative Adversarial Network
Venue: arXiv cs.CV 2019
Year: 2019

We propose a new approach to Generative Adversarial Networks (GANs) to achieve an improved performance with additional robustness to its so-called and well recognized mode collapse. We first proceed by mapping the desired data onto a frame-based space for a sparse representation to lift any limitation of small support features prior to learning the structure. To that end we start by dividing an image into multiple patches and modifying the role of the generative network from producing an entire image, at once, to creating a sparse representation vector for each image patch. We synthesize an entire image by multiplying generated sparse representations to a pre-trained dictionary and assembling the resulting patches. This approach restricts the output of the generator to a particular structure, obtained by imposing a Union of Subspaces (UoS) model to the original training data, leading to more realistic images, while maintaining a desired diversity. To further regularize GANs in generating high-quality images and to avoid the notorious mode-collapse problem, we introduce a third player in GANs, called reconstructor. This player utilizes an auto-encoding scheme to ensure that first, the input-output relation in the generator is injective and second each real image corresponds to some input noise. We present a number of experiments, where the proposed algorithm shows a remarkably higher inception score compared to the equivalent conventional GANs.
Deploying Technology to Save Endangered Languages
Venue: arXiv cs.CL 2019
Year: 2019

Computer scientists working on natural language processing, native speakers of endangered languages, and field linguists to discuss ways to harness Automatic Speech Recognition, especially neural networks, to automate annotation, speech tagging, and text parsing on endangered languages.
FaSTExt: Fast and Small Text Extractor
Venue: arXiv cs.CV 2019
Year: 2019

Text detection in natural images is a challenging but necessary task for many applications. Existing approaches utilize large deep convolutional neural networks making it difficult to use them in real-world tasks. We propose a small yet relatively precise text extraction method. The basic component of it is a convolutional neural network which works in a fully-convolutional manner and produces results at multiple scales. Each scale output predicts whether a pixel is a part of some word, its geometry, and its relation to neighbors at the same scale and between scales. The key factor of reducing the complexity of the model was the utilization of depthwise separable convolution, linear bottlenecks, and inverted residuals. Experiments on public datasets show that the proposed network can effectively detect text while keeping the number of parameters in the range of 1.58 to 10.59 million in different configurations.
Gradient Weighted Superpixels for Interpretability in CNNs
Venue: arXiv cs.CV 2019
Year: 2019

As Convolutional Neural Networks embed themselves into our everyday lives, the need for them to be interpretable increases. However, there is often a trade-off between methods that are efficient to compute but produce an explanation that is difficult to interpret, and those that are slow to compute but provide a more interpretable result. This is particularly challenging in problem spaces that require a large input volume, especially video which combines both spatial and temporal dimensions. In this work we introduce the idea of scoring superpixels through the use of gradient based pixel scoring techniques. We show qualitatively and quantitatively that this is able to approximate LIME, in a fraction of the time. We investigate our techniques using both image classification, and action recognition networks on large scale datasets (ImageNet and Kinetics-400 respectively).